Tabular Learning Revisited: An Empirical Study of Tabular Classification

Abstract

Tabular data represent one of the most prevalent data formats in applied machine learning, largely because they accommodate a broad spectrum of real-world problems.
Existing literature has studied many of the shortcomings of neural architectures on tabular data and has repeatedly confirmed the scalability and robustness of gradient-boosted decision trees across varied datasets. However, recent deep learning models have not been subjected to a comprehensive evaluation under conditions that allow for a fair comparison with existing classical approaches. This situation motivates an investigation into whether recent deep-learning paradigms outperform classical ML methods on tabular data. Our survey fills this gap by benchmarking twenty state-of-the-art methods, spanning neural networks, classical ML and AutoML techniques. Our empirical results over 68 diverse classification datasets from a well-established benchmark indicate a paradigm shift, where Deep Learning methods outperform classical approaches.